Title:
Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negatives

Abstract: In this paper we propose a novel method that provides contrastive
explanations justifying the classification of an input by a black box
classifier such as a deep neural network. Given an input we find what should be
minimally and sufficiently present (viz. important object pixels in an image)
to justify its classification and analogously what should be minimally and
necessarily \emph{absent} (viz. certain background pixels). We argue that such
explanations are natural for humans and are used commonly in domains such as
health care and criminology. What is minimally but critically \emph{absent} is
an important part of an explanation, which to the best of our knowledge, has
not been touched upon by current explanation methods that attempt to explain
predictions of neural networks. We validate our approach on three real datasets
obtained from diverse domains; namely, a handwritten digits dataset MNIST, a
large procurement fraud dataset and an fMRI brain imaging dataset. In all three
cases, we witness the power of our approach in generating precise explanations
that are also easy for human experts to understand and evaluate.